This disclosure relates generally to the field of image processing and, more particularly, to various techniques for use in adaptively auto exposing and improving the signal-to-noise ratio of digital images captured by an image sensor, e.g., in a personal electronic device.
Today, many personal electronic devices come equipped with digital cameras. Often, these devices perform many functions, and, as a consequence, the digital image sensors included in these devices must often be smaller than the sensors in conventional cameras. Further, the camera hardware in these devices often have smaller dynamic ranges and lack sophisticated features sometimes found in larger, professional-style conventional cameras such as manual exposure controls and manual focus. Thus, it is important that digital cameras in personal electronic devices be able to produce visually appealing images in a wide variety of lighting and scene situations with limited or no interaction from the user, as well as in a computationally and cost-effective manner.
One feature that has been implemented in some digital cameras to compensate for lack of dynamic range and create visually appealing images is known as “auto exposure.” Auto exposure (AE) can be defined generally as any algorithm that automatically calculates and/or manipulates certain camera exposure parameters, e.g., exposure time, ISO, or f-number, in such a way that the currently exposed scene is captured in a desirable manner.
Auto exposure algorithms are often employed in conjunction with image sensors having small dynamic ranges because the dynamic range of light in a given scene, i.e., from absolute darkness to bright sunlight, is much larger than the range of light that some image sensors—such as those often found in personal electronic devices—are capable of capturing. However, the exposure parameter adjustments, e.g., gain adjustments, implemented by traditional AE algorithms may cause the signal to be “clipped,” that is, be pushed to a value above (or below) what the camera's hardware is capable of storing. This clipping process can result in the loss of valuable image detail.
In addition to the above-mentioned clipping issue, image noise is another challenge the image processing pipeline needs to deal with. The use of longer sensor integration time is one technique that may be employed to attempt to enhance the image signal while reducing the random noise. Often, however, image processing pipelines do not fully utilize this image sensor behavior when the scene to be captured allows for longer integration time before signal clipping occurs.
The inventors have realized new and non-obvious ways to achieve lower noise image captures and process the resulting captured images without clipping the image signal. The inventors have also realized new and non-obvious ways to perform lens shading correction (LSC) operations that modulate gains based on scene lux level and lens focus distance.